Collaborative Research: A Bayesian model of phonetic and phonotactic effects in cross-language speech production

协作研究：跨语言语音生成中语音和语音趋向效应的贝叶斯模型

• 批准号: 1052855
• 负责人: Lisa Davidson
• 金额: $ 17.49万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1052855&HistoricalAwards=false
• 关键词: Collaborative; Research; Bayesian; model; phonetic

A fundamental aspect of human cognition is the capacity to perceive and produce language. Studies of non-native speech processing provide some of the most striking evidence bearing on this capacity: when humans attempt to perceive or produce words containing foreign sounds or sound sequences, they show systematic patterns of correct and incorrect performance. Prior research has established that different non-native structures elicit different rates and types of error; it has been hypothesized that these differences can be explained by a combination of grammatical, perceptual, and articulatory factors. The main goals of this project are to provide carefully controlled experimental evaluations of these factors, and to develop an explicit, probabilistic model of how they interact in human performance. Particular experimental issues to be investigated are: (1) what phonetic characteristics humans are most sensitive to when processing non-native sounds; (2) how the quality of the input and ambient acoustics affect non-native perception and production; and (3) whether learning word meaning can modulate sensitivity to detailed properties of non-native sounds. The computational model builds on a growing body of work suggesting that human perception and action reflect optimal Bayesian inference conditioned on prior expectations and noisy sensory measurements. The relevant prior reflects knowledge of the native language; the model predicts that non-native structures that are more similar to those in the native language should be processed with greater accuracy. The model also predicts that non-native sounds with robust perceptual properties should be processed more accurately, even if their prior probabilities are low. The development of this model, which will be made available to other researchers, will promote the role of phonology and phonetics within the broader context of cognitive science research. Because our experiments examine the impact of classroom acoustics and talker variation on non-native sound processing, this project also has ramifications for foreign language pedagogy.

人类认知的一个基本方面是感知和产生语言的能力。 对非母语语音处理的研究提供了一些与这种能力有关的最引人注目的证据：当人类试图感知或产生包含外国声音或声音序列的单词时，他们表现出正确和不正确的系统模式。先前的研究已经确定，不同的非母语结构会导致不同的错误率和类型;有人假设，这些差异可以通过语法，感知和发音因素的组合来解释。该项目的主要目标是提供这些因素的仔细控制的实验评估，并开发一个明确的，概率模型，它们如何在人类的表现相互作用。具体的实验问题是：（1）当处理非母语声音时，人类对哪些语音特征最敏感;（2）输入和环境声学的质量如何影响非母语的感知和产生;（3）学习词义是否可以调节对非母语声音细节属性的敏感性。计算模型建立在越来越多的工作表明，人类的感知和行动反映了最佳的贝叶斯推理条件事先的期望和嘈杂的感官测量。相关先验反映了母语的知识;该模型预测，与母语中的结构更相似的非母语结构应该以更高的准确性进行处理。该模型还预测，具有鲁棒感知特性的非母语声音应该被更准确地处理，即使它们的先验概率很低。这一模型的发展，将提供给其他研究人员，将促进音韵学和语音学在更广泛的认知科学研究背景下的作用。因为我们的实验研究了教室声学和说话者变化对非母语声音处理的影响，这个项目也对外语教学产生了影响。